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Accurately typing entity mentions from text segments is a fundamental taskfor various natural language processing applications. Many previous approachesrely on massive human-annotated data to perform entity typing. Nevertheless,collecting such data in highly specialized science and engineering domains(e.g., software engineering and security) can be time-consuming and costly,without mentioning the domain gaps between training and inference data if themodel needs to be applied to confidential datasets. In this paper, we study thetask of seed-guided fine-grained entity typing in science and engineeringdomains, which takes the name and a few seed entities for each entity type asthe only supervision and aims to classify new entity mentions into both seenand unseen types (i.e., those without seed entities). To solve this problem, wepropose SEType which first enriches the weak supervision by finding moreentities for each seen type from an unlabeled corpus using the contextualizedrepresentations of pre-trained language models. It then matches the enrichedentities to unlabeled text to get pseudo-labeled samples and trains a textualentailment model that can make inferences for both seen and unseen types.Extensive experiments on two datasets covering four domains demonstrate theeffectiveness of SEType in comparison with various baselines.

Seed-Guided Fine-Grained Entity Typing in Science and Engineering Domains